A parallel quasi-Monte Carlo approach to pricing multidimensional American options
International Journal of High Performance Computing and Networking
High Performance Implementation of Binomial Option Pricing
ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
Parallelization of Pricing Path-Dependent Financial Instruments on Bounded Trinomial Lattices
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part II
Cache-optimal algorithms for option pricing
ACM Transactions on Mathematical Software (TOMS)
Evaluating multicore algorithms on the unified memory model
Scientific Programming - Software Development for Multi-core Computing Systems
Ant colony optimization to price exotic options
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Mathematics and Computers in Simulation
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In this paper, we develop parallel algorithms for pricing a class of multidimensional financial derivatives employing binomial lattice approach. We describe the algorithms, explain their complexities, and study their performance. The limitations posed by the problem size on the recursive algorithm and the solution to overcome this problem by the iterative algorithm are explained through the experimental results using MPI. We first present analytical results for the number of computations and communications for both the algorithms. Since the number of nodes in a recombining lattice grows linearly with the problem size, it is feasible to price long-dated options using a recombining lattice. We have extended our algorithm to handle derivatives with many underlying assets and shown that the multi-asset derivatives offer a better problem for parallel computation. This is very important for finance industry since long-dated derivatives with many underlying assets are common in practice.